Abstract\ud This thesis presents the research and subsequent development of a real life system\ud capable of identifying and monitoring objects for user-specified scenarios in live CCTV\ud video. More specifically, after a review of the state of the art in both academic methods\ud and commercial systems, two main novel aspects of the proposed system are detailed.\ud The first deals with the detection of vehicles in static images using a combination of\ud features including: corners, lines and colour. The second aspect relates to how motion\ud has been exploited to detect and track objects within video feeds.\ud The research took place as part of a Knowledge Transfer Partnership, the partner\ud company of which had access to many video feeds across a range of different\ud geographical locations. This provided the opportunity elicit requirements from the actual\ud end-users and to extensively test and evaluate the system on real data.\ud Results from both video and static analysis systems are demonstrated and evaluated\ud before the Thesis concludes with specific enhancements that could be made to the\ud current system and general recommendations for future work.